r/MachineLearning · 2019
Most-mentioned books in r/MachineLearning during 2019
This page ranks books by exact tracked mentions in r/MachineLearning within the published source unit for 2019. Counts come from the reconciled subreddit-by-month matrix; a source unit may be a bounded sample.
#1 Pattern Recognition and Machine Learning
Christopher M. Bishop
Bishop's 2006 Bayesian ML textbook: 26 of its 39 Reddit mentions come from r/MachineLearning, where it sits permanently on the serious-math shelf.
#2 Cracking the Coding Interview
Gayle Laakmann McDowell
The book that turned software hiring into a sport — written by the ex-Googler who introduced programming challenges to the process in the first place.
#3 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Aurélien Géron
Aurélien Géron's end-to-end ML textbook: build a working neural net with Scikit-Learn, Keras, and TensorFlow before you finish the first half.
#4 Code Complete
Steve McConnell
Steve McConnell's 900-page construction manual for software, ranked third on Reddit's canonical reading list and still cited 25 years after publication.
#5 Designing Data-Intensive Applications
Martin Kleppmann
A backend engineer's field guide to the tradeoffs behind every database, queue, and distributed system you will ever touch.
#6 Introduction to Algorithms
Thomas H. Cormen
The textbook four MIT professors wrote to settle every algorithms argument, cited by 254 Reddit commenters across 12 subreddits and still going.
#7 Refactoring
Martin Fowler
Martin Fowler's catalog of named moves for cleaning up code that already works, cited 116 times across Reddit in 7 years as the Clean Code alternative people actually prefer.
#8 The Mythical Man-Month
Frederick P. Brooks Jr.
Fred Brooks spent the 1960s building IBM's OS/360 and wrote down everything that went wrong — in 1975, and it still lands.
#9 The Pragmatic Programmer
Andy Hunt
Two veterans hand you a checklist for the craft: don't write code you don't need, own your tools, and fix the broken window before someone else does.